class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE (GAS) RANK MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame
      Field to specify the univariate time series data that will be used.
  
  team_1 : str (pd.DataFrame)        
      Specifies which column contains the home team
  
  team_2 : str (pd.DataFrame)        
      Specifies which column contains the away team
  
  family : GAS family object
      Which distribution to use, e.g. GASNormal()
  
  score_diff : str (pd.DataFrame)        
      Specifies which column contains the score
  
  gradient_only : Boolean (default: True)
      If true, will only use gradient rather than second-order terms
      to construct the modified score.
docstr: Creates IDs for both players/teams
docstr_tok: ["Creates","IDs","for","both","players\/teams"]
code: def _create_ids(self, home_teams, away_teams):
      """
      Creates IDs for both players/teams
      """
      categories = pd.Categorical(np.append(home_teams, away_teams))
      home_id, away_id = (
          categories.codes[0 : int(len(categories) / 2)],
          categories.codes[
              int(len(categories) / 2) : len(categories) + 1
          ],
      )
      return home_id, away_id
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .scores import *
  from .gas_core_recursions import gas_recursion
label_desc: Use image node_id 2 for calling the GASRank obj's underlying member method code with example usage: obj._create_ids(home_teams, away_teams) and returns: home_id,  away_id